library(Seurat)
library(SeuratDisk)
library(reticulate)
library(scrubletR)
library(ggplot2)
library(patchwork)
library(dplyr)
library(data.table)
library(clustree)
library(reshape2)
library(tidyr)
library(gridExtra)
library(stringr)
library(plyr)
source("C:/Ryan/GitHub/trachtenberg-lab/transcriptomics/tools/seurat_functions.R")
source("C:/Ryan/GitHub/trachtenberg-lab/transcriptomics/tools/seurat_integration_functions.R")
source("C:/Ryan/GitHub/trachtenberg-lab/transcriptomics/xgboost/xgboost_train.R")
source("C:/Ryan/GitHub/trachtenberg-lab/transcriptomics/xgboost/plottingFxns.R")

classes <- c("Glutamatergic", "GABAergic", "Nonneuronal")
objs.mouse <- c()
objs.opossum <- c()
objs.i.mouse <- c()
objs.i.opossum <- c()
objs.m.mouse <- c()
objs.m.opossum <- c()

for (cl in classes) {

  obj.opossum <- readRDS(paste0("E:/Transcriptomics_V1/Opossum/seurat/opossum_v1_", tolower(cl), "_processed.rds"))
  obj.opossum$species <- "Opossum"
  obj.mouse.P38 <- readRDS(paste0("E:/Transcriptomics_V1/Mouse/seurat/mouse_v1_P38_", tolower(cl), "_processed.rds"))
  obj.mouse.P38$species <- "Mouse"

  objs <- list(obj.opossum, obj.mouse.P38)
  obj.integrated <- IntegrateObjects(objs[[1]], objs[[2]], resolutions = c(1, 2), nfeatures = 3000, subsample = TRUE)
  objs.i <- SplitObject(obj.integrated, split.by = "species")
  objs.m <- MapObjects(objs.i[["Opossum"]], objs.i[["Mouse"]], c("subclass", "type"), assay = "integrated")
  objs.mouse <- append(objs.mouse, objs[[2]])
  objs.opossum <- append(objs.opossum, objs[[1]])
  objs.i.mouse <- append(objs.i.mouse, objs.i[["Mouse"]])
  objs.i.opossum <- append(objs.i.opossum, objs.i[["Opossum"]])
  objs.m.mouse <- append(objs.m.mouse, objs.m[[2]])
  objs.m.opossum <- append(objs.m.opossum, objs.m[[1]])
  
}

obj.mouse <- merge(objs.mouse[[1]], y = c(objs.mouse[[2]], objs.mouse[[3]]))
obj.opossum <- merge(objs.opossum[[1]], y = c(objs.opossum[[2]], objs.opossum[[3]]))
obj.m.mouse <- merge(objs.m.mouse[[1]], y = c(objs.m.mouse[[2]], objs.m.mouse[[3]]))
obj.m.opossum <- merge(objs.m.opossum[[1]], y = c(objs.m.opossum[[2]], objs.m.opossum[[3]]))

obj.mouse <- ClusterSCT(obj.mouse, c(1))
obj.m.mouse <- ClusterSCT(obj.m.mouse, c(1))
obj.opossum <- ClusterSCT(obj.opossum, c(1))
obj.m.opossum <- ClusterSCT(obj.m.opossum, c(1))

obj.mouse <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_mouse.rds")
Loading required package: SeuratObject
Loading required package: sp
obj.opossum <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_opossum.rds")
objs.i.mouse <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_i_mouse.rds")
objs.i.opossum <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_i_opossum.rds")
objs.m.mouse <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/objs_m_mouse.rds")
objs.m.opossum <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/objs_m_opossum.rds")
obj.m.mouse <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_m_mouse.rds")
obj.m.opossum <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_m_opossum.rds")

colors_list <- list(
              # Glutamatergic
              IT = "#FF6C88",
              IT_A = "#FFB3B3",
              `L2/3` = "#FFB3B3",
              IT_B = "#FFA07A",
              L4 = "#FF7F50",
              IT_C = "#FF7F50",
              L5IT = "#FFA07A",
              IT_D = "#FF6347",
              L6IT = "#FF6347",
              L5NP = "#FF4500",
              L5PT = "#FF8C69",
              L6CT = "#FFA07A",
              L6b = "#FF6347",
            
              # GABAergic
              Pvalb = "#1E90FF",
              Sst = "#87CEEB",
              Vip = "#87CEFA",
              Lamp5 = "#4682B4",
              Frem1 = "#ADD8E6",
              Stac = "#5F9EA0",
            
              # Non-neuronal
              Astro = "#8C8C8C",
              Micro = "#A0A0A0",
              OD = "#B4B4B4",
              OPC = "#C8C8C8",
              Endo = "#505050",
              VLMC = "#B4B4B4"
)

obj.mouse$species <- "Mouse"
obj.opossum$species <- "Opossum"
obj.combined <- merge(obj.mouse, y = obj.opossum)
obj.combined$subclass.plot <- obj.combined$subclass
obj.combined$subclass.plot[obj.combined$subclass %in% c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E", 
                                                   "L2/3", "L4", "L5IT", "L6IT")] <- "IT"

Idents(obj.combined) <- "subclass.plot"
levels(obj.combined) <- rev(c("IT", "L5NP", "L5PT", "L6CT", "L6b",
                              "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac", 
                              "Astro", "Micro", "OD", "OPC", "Endo", "VLMC"))
DotPlot(obj.combined, assay = "RNA", features = c("Snap25", "Sv2b", "Cux2", "Rorb", "Deptor", "Etv1", "Trpc4", "Foxp2", "Lin28b", 
                                                  "Gad1", "Myo5b", "Sst", "Vip", "Lamp5", "Frem1", "Stac", 
                                                  "Aldh1l1", "Cx3cr1", "Mog", "Pdgfra", "Flt1", "Slc47a1"), cols = c("#a6a6a6", "#c692b8"), split.by = "species", scale = TRUE) + theme(axis.text.x=element_text(angle = 90, vjust = 0.4))


obj.opossum$subclass.plot <- obj.opossum$subclass
obj.opossum$subclass.plot[obj.opossum$subclass %in% c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E", 
                                                        "L2/3", "L4", "L5IT", "L6IT")] <- "IT"
opossum.counts <- as.data.frame.matrix(table(obj.opossum$sample, obj.opossum$subclass.plot))
opossum.counts$Stac <- 0
opossum.counts$VLMC <- 0
opossum.counts$species <- "Opossum"
opossum.counts$Glutamatergic <- rowSums(opossum.counts[, c("IT", "L5NP", "L5PT", "L6CT", "L6b")])
opossum.counts$GABAergic <- rowSums(opossum.counts[, c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac")])
opossum.counts$Nonneuronal <- rowSums(opossum.counts[, c("Astro", "Micro", "OD", "OPC", "Endo", "VLMC")])

obj.mouse$subclass.plot <- obj.mouse$subclass
obj.mouse$subclass.plot[obj.mouse$subclass %in% c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E", 
                                                        "L2/3", "L4", "L5IT", "L6IT")] <- "IT"
mouse.counts <- as.data.frame.matrix(table(obj.mouse$sample, obj.mouse$subclass.plot))
mouse.counts$species <- "Mouse"
mouse.counts$Glutamatergic <- rowSums(mouse.counts[, c("IT", "L5NP", "L5PT", "L6CT", "L6b")])
mouse.counts$GABAergic <- rowSums(mouse.counts[, c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac")])
mouse.counts$Nonneuronal <- rowSums(mouse.counts[, c("Astro", "Micro", "OD", "OPC", "Endo", "VLMC")])

all.counts <- rbind(opossum.counts, mouse.counts)

all.props <- all.counts %>%
  mutate(IT = IT / Glutamatergic,
         L5NP = L5NP / Glutamatergic,
         L5PT = L5PT / Glutamatergic,
         L6CT = L6CT / Glutamatergic,
         L6b = L6b / Glutamatergic,
         Pvalb = Pvalb / GABAergic,
         Sst = Sst / GABAergic,
         Vip = Vip / GABAergic,
         Lamp5 = Lamp5 / GABAergic,
         Frem1 = Frem1 / GABAergic, 
         Stac = Stac / GABAergic, 
         Astro = Astro / Nonneuronal,
         Micro = Micro / Nonneuronal,
         OD = OD / Nonneuronal,
         OPC = OPC / Nonneuronal,
         Endo = Endo / Nonneuronal
         )

scatter.props <- all.props

all.props.long <- all.props %>%
                  pivot_longer(
                    cols = c("IT", "L5NP", "L5PT", "L6CT", "L6b", 
                             "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac", 
                             "Astro", "Micro", "OD", "OPC", "Endo", "VLMC", 
                             "Glutamatergic", "GABAergic", "Nonneuronal"),
                    names_to = "cell.identity", 
                    values_to = "fraction"
                               )

all.props.long$species <- factor(all.props.long$species, levels = c("Opossum", "Mouse"))

curr.props <- all.props.long[all.props.long$cell.identity %in% c("IT", "L5NP", "L5PT", "L6CT", "L6b"), ]

ggplot(curr.props, aes(x = factor(cell.identity, level = c("IT", "L5NP", "L5PT", "L6CT", "L6b")), y = fraction, fill = species)) +
    geom_bar(stat = "summary", fun = "median", position = position_dodge(width = 0.8), width = 0.75) +
    geom_point(colour = "black", position = position_dodge(width = 0.8), size = 1) +
    scale_x_discrete(labels = c("IT", "L5NP", "L5PT", "L6CT", "L6b")) +
    scale_y_continuous(breaks = c(0, 0.2, 0.4, 0.6, 0.8), expand = c(0, 0), limits = c(0, 0.80)) +
    guides(fill = guide_legend(override.aes = list(shape = NA))) +
    theme_classic() + theme(axis.text = element_text(color = "black"), legend.text = element_text(size = 10)) + 
    labs(x = NULL, y = "Fraction of Glutamatergic Cells", fill = NULL) + 
    scale_fill_manual("legend", values = c("Opossum" = "#c692b8", "Mouse" = "#aaaaaa"))


all.props.long$species <- factor(all.props.long$species, levels = c("Opossum", "Mouse"))

curr.props <- all.props.long[all.props.long$cell.identity %in% c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac"), ]

ggplot(curr.props, aes(x = factor(cell.identity, level = c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac")), y = fraction, fill = species)) +
    geom_bar(stat = "summary", fun = "median", position = position_dodge(width = 0.8), width = 0.75) +
    geom_point(colour = "black", position = position_dodge(width = 0.8), size = 1) +
    scale_x_discrete(labels = c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac")) +
    scale_y_continuous(breaks = c(0, 0.2, 0.4, 0.6), expand = c(0, 0), limits = c(0, 0.60)) +
    guides(fill = guide_legend(override.aes = list(shape = NA))) +
    theme_classic() + theme(axis.text = element_text(color = "black"), legend.text = element_text(size = 10)) + 
    labs(x = NULL, y = "Fraction of GABAergic Cells", fill = NULL) + 
    scale_fill_manual("legend", values = c("Opossum" = "#c692b8", "Mouse" = "#aaaaaa"))


all.props.long$species <- factor(all.props.long$species, levels = c("Opossum", "Mouse"))

curr.props <- all.props.long[all.props.long$cell.identity %in% c("Astro", "Micro", "OD", "OPC", "Endo", "VLMC"), ]

ggplot(curr.props, aes(x = factor(cell.identity, level = c("Astro", "Micro", "OD", "OPC", "Endo", "VLMC")), y = fraction, fill = species)) +
    geom_bar(stat = "summary", fun = "median", position = position_dodge(width = 0.8), width = 0.75) +
    geom_point(colour = "black", position = position_dodge(width = 0.8), size = 1) +
    scale_x_discrete(labels = c("Astro", "Micro", "OD", "OPC", "Endo", "VLMC")) +
    scale_y_continuous(breaks = c(0, 0.2, 0.4, 0.6, 0.8), expand = c(0, 0), limits = c(0, 0.60)) +
    guides(fill = guide_legend(override.aes = list(shape = NA))) +
    theme_classic() + theme(axis.text = element_text(color = "black"), legend.text = element_text(size = 10)) + 
    labs(x = NULL, y = "Fraction of Nonneuronal Cells", fill = NULL) + 
    scale_fill_manual("legend", values = c("Opossum" = "#c692b8", "Mouse" = "#aaaaaa"))


opossum.counts <- as.data.frame.matrix(table(obj.opossum.IT$sample, obj.opossum.IT$subclass))
opossum.counts$`L2/3` <- opossum.counts$IT_A
opossum.counts$L4 <- opossum.counts$IT_C
opossum.counts$L5IT <- opossum.counts$IT_B
opossum.counts$L6IT <- opossum.counts$IT_D
opossum.counts$species <- "Opossum"
opossum.counts$Glutamatergic <- rowSums(opossum.counts[, c("L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b")])

mouse.counts <- as.data.frame.matrix(table(obj.mouse.IT$sample, obj.mouse.IT$subclass))
mouse.counts$IT_A <- mouse.counts$`L2/3`
mouse.counts$IT_C <- mouse.counts$L4
mouse.counts$IT_B <- mouse.counts$L5IT
mouse.counts$IT_D <- mouse.counts$L6IT
mouse.counts$species <- "Mouse"
mouse.counts$Glutamatergic <- rowSums(mouse.counts[, c("L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b")])

all.counts <- rbind(opossum.counts, mouse.counts)

all.props <- all.counts %>%
  mutate(`L2/3` = `L2/3` / Glutamatergic,
         L4 = L4 / Glutamatergic,
         L5IT = L5IT / Glutamatergic,
         L6IT = L6IT / Glutamatergic,
         L5NP = L5NP / Glutamatergic,
         L5PT = L5PT / Glutamatergic,
         L6CT = L6CT / Glutamatergic,
         L6b = L6b / Glutamatergic
         )

scatter.props <- all.props

all.props.long <- all.props %>%
                  pivot_longer(
                    cols = c("L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b"),
                    names_to = "cell.identity", 
                    values_to = "fraction"
                               )

all.props.long$species <- factor(all.props.long$species, levels = c("Opossum", "Mouse"))

curr.props <- all.props.long[all.props.long$cell.identity %in% c("L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b"), ]

ggplot(curr.props, aes(x = factor(cell.identity, level = c("L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b")), y = fraction, fill = species)) +
    geom_bar(stat = "summary", fun = "median", position = position_dodge(width = 0.8), width = 0.75) +
    geom_point(colour = "black", position = position_dodge(width = 0.8), size = 1) +
    scale_x_discrete(labels = c("L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b")) +
    scale_y_continuous(breaks = c(0, 0.2, 0.4, 0.6), expand = c(0, 0), limits = c(0, 0.60)) +
    guides(fill = guide_legend(override.aes = list(shape = NA))) +
    theme_classic() + theme(axis.text = element_text(color = "black"), legend.text = element_text(size = 10)) + 
    labs(x = NULL, y = "Fraction of Glutamatergic Cells", fill = NULL) + 
    scale_fill_manual("legend", values = c("Opossum" = "#c692b8", "Mouse" = "#aaaaaa"))


DimPlot(obj.mouse, reduction = "umap", group.by = "sample", label = FALSE, raster = FALSE, shuffle = TRUE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.mouse, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.mouse, reduction = "umap", group.by = "type", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

obj.mouse$subclass.IT <- obj.mouse$subclass
obj.mouse$subclass.IT[obj.mouse$subclass %in% c("L2/3", "L4", "L5IT", "L6IT")] <-  "IT"
p <- DimPlot(obj.mouse, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_Subclass_FullSpace.png", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_Subclass_FullSpace.svg", plot = p, dpi = 300)
p <- DimPlot(obj.mouse, reduction = "umap", group.by = "subclass.IT", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_SubclassIT_FullSpace.png", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_SubclassIT_FullSpace.svg", plot = p, dpi = 300)

obj.opossum$subclass.plot <- obj.opossum$subclass
obj.opossum$subclass.plot[obj.opossum$subclass %in% c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E", 
                                                      "L2/3", "L4", "L5IT", "L6IT")] <- "IT"

opossum.subclass.plot.levels <- c("IT", "L5NP", "L5PT", "L6CT", "L6b", 
                                  "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", 
                                  "Astro", "Micro", "OD", "OPC", "Endo")

opossum.subclass.levels <- c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E", "L5NP", "L5PT", "L6CT", "L6b", 
                                  "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", 
                                  "Astro", "Micro", "OD", "OPC", "Endo")

Idents(obj.opossum) <- "subclass.plot"
levels(obj.opossum) <- opossum.subclass.plot.levels
colors <- as.character(colors_list[opossum.subclass.plot.levels])
DimPlot(obj.opossum, reduction = "umap", label = TRUE, raster = FALSE, shuffle = TRUE, cols = colors) + NoLegend() + xlim(-17, 13) + ylim(-13, 17) + coord_equal()


opossum.subclass.levels <- c("IT_A", "IT_B", "IT_C", "IT_D", "L5NP", "L5PT", "L6CT", "L6b", 
                             "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", 
                             "Astro", "Micro", "OD", "OPC", "Endo")

Idents(obj.m.opossum) <- "subclass"
levels(obj.m.opossum) <- opossum.subclass.levels
colors <- as.character(colors_list[opossum.subclass.levels])
DimPlot(obj.m.opossum, reduction = "umap", label = TRUE, raster = FALSE, shuffle = TRUE, cols = colors) + NoLegend() + xlim(-16, 13) + ylim(-12, 17) + coord_equal()


mouse.subclass.levels <- c("L2/3", "L4", "L6IT", "L5PT", "L6CT", "L6b", 
                           "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac",
                           "Astro", "Micro", "OD", "OPC", "Endo")

Idents(obj.m.opossum) <- "predicted.subclass"
levels(obj.m.opossum) <- mouse.subclass.levels
colors <- as.character(colors_list[mouse.subclass.levels])
DimPlot(obj.m.opossum, reduction = "umap", label = TRUE, raster = FALSE, shuffle = TRUE, cols = colors) + NoLegend() + xlim(-16, 13) + ylim(-12, 17) + coord_equal()


obj.mouse$subclass.plot <- obj.mouse$subclass
obj.mouse$subclass.plot[obj.mouse$subclass %in% c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E", 
                                                      "L2/3", "L4", "L5IT", "L6IT")] <- "IT"

mouse.subclass.plot.levels <- c("IT", "L5NP", "L5PT", "L6CT", "L6b", 
                                "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac",
                                "Astro", "Micro", "OD", "OPC", "Endo", "VLMC")

mouse.subclass.levels <- c("L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b", 
                           "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac",
                           "Astro", "Micro", "OD", "OPC", "Endo", "VLMC")

Idents(obj.mouse) <- "subclass.plot"
levels(obj.mouse) <- mouse.subclass.plot.levels
colors <- as.character(colors_list[mouse.subclass.plot.levels])
DimPlot(obj.mouse, reduction = "umap", label = TRUE, raster = FALSE, shuffle = TRUE, cols = colors) + NoLegend() + xlim(-16, 16) + ylim(-17, 15) + coord_equal()


mouse.subclass.levels <- c("L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b", 
                           "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac", 
                           "Astro", "Micro", "OD", "OPC", "Endo", "VLMC")

Idents(obj.m.mouse) <- "subclass"
levels(obj.m.mouse) <- mouse.subclass.levels
colors <- as.character(colors_list[mouse.subclass.levels])
DimPlot(obj.m.mouse, reduction = "umap", label = TRUE, raster = FALSE, shuffle = TRUE, cols = colors) + NoLegend() + xlim(-14, 18) + ylim(-16, 16) + coord_equal()


DimPlot(obj.m.mouse, reduction = "umap", group.by = "sample", label = FALSE, raster = FALSE, shuffle = TRUE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.mouse, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.mouse, reduction = "umap", group.by = "predicted.subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.mouse, reduction = "umap", group.by = "type", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

p <- DimPlot(obj.m.mouse, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_Subclass_IntSpace.svg", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_Subclass_IntSpace.png", plot = p, dpi = 300)
p <- DimPlot(obj.m.mouse, reduction = "umap", group.by = "predicted.subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_PredictedSubclass_IntSpace.svg", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_PredictedSubclass_IntSpace.png", plot = p, dpi = 300)

DimPlot(obj.opossum, reduction = "umap", group.by = "sample", label = FALSE, raster = FALSE, shuffle = TRUE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.opossum, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.opossum, reduction = "umap", group.by = "type", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

obj.opossum$subclass.IT <- obj.opossum$subclass
obj.opossum$subclass.IT[obj.opossum$subclass %in% c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E")] <-  "IT"
p <- DimPlot(obj.opossum, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_Subclass_FullSpace.png", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_Subclass_FullSpace.svg", plot = p, dpi = 300)
p <- DimPlot(obj.opossum, reduction = "umap", group.by = "subclass.IT", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_SubclassIT_FullSpace.png", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_SubclassIT_FullSpace.svg", plot = p, dpi = 300)

DimPlot(obj.m.opossum, reduction = "umap", group.by = "sample", label = FALSE, raster = FALSE, shuffle = TRUE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.opossum, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.opossum, reduction = "umap", group.by = "predicted.subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.opossum, reduction = "umap", group.by = "type", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

p <- DimPlot(obj.m.opossum, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_Subclass_IntSpace.svg", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_Subclass_IntSpace.png", plot = p, dpi = 300)
p <- DimPlot(obj.m.opossum, reduction = "umap", group.by = "predicted.subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_PredictedSubclass_IntSpace.svg", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_PredictedSubclass_IntSpace.png", plot = p, dpi = 300)

PlotMapping(list(objs.m.mouse[[1]], objs.m.opossum[[1]]), ident.order = c("IT_A", "IT_B", "IT_C", "IT_D", "L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b"))


PlotMapping(list(objs.m.mouse[[2]], objs.m.opossum[[2]]), ident.order = c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac"))


PlotMapping(list(objs.m.mouse[[3]], objs.m.opossum[[3]]), ident.order = c("Astro", "Micro", "OD", "OPC", "Endo", "VLMC"))


opossum.glutamatergic.levels <- c("IT_A", "IT_B", "IT_C", "IT_D", "L5NP", "L5PT", "L6CT", "L6b")
Idents(objs.m.opossum[[1]]) <- "subclass"
levels(objs.m.opossum[[1]]) <- opossum.glutamatergic.levels
VlnPlot(objs.m.opossum[[1]], "predicted.subclass.score", cols = colors_list[opossum.glutamatergic.levels]) + theme(legend.spacing.x = unit(3.0, 'cm'))


opossum.gabaergic.levels <- c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1")
Idents(objs.m.opossum[[2]]) <- "subclass"
levels(objs.m.opossum[[2]]) <- opossum.gabaergic.levels
VlnPlot(objs.m.opossum[[2]], "predicted.subclass.score", cols = colors_list[opossum.gabaergic.levels]) + theme(legend.spacing.x = unit(6.5, 'cm'))


opossum.nonneuronal.levels <- c("Astro", "Micro", "OD", "OPC", "Endo")
Idents(objs.m.opossum[[3]]) <- "subclass"
levels(objs.m.opossum[[3]]) <- opossum.nonneuronal.levels
VlnPlot(objs.m.opossum[[3]], "predicted.subclass.score", cols = colors_list[opossum.nonneuronal.levels]) + theme(legend.spacing.x = unit(6.5, 'cm'))


saveRDS(obj.mouse, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_mouse.rds")
saveRDS(obj.opossum, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_opossum.rds")
saveRDS(objs.i.mouse, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_i_mouse.rds")
saveRDS(objs.i.opossum, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_i_opossum.rds")
saveRDS(objs.m.mouse, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/objs_m_mouse.rds")
saveRDS(objs.m.opossum, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/objs_m_opossum.rds")
saveRDS(obj.m.mouse, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_m_mouse.rds")
saveRDS(obj.m.opossum, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_m_opossum.rds")
---
title: "R Notebook"
output: html_notebook
---


```{r}

library(Seurat)
library(SeuratDisk)
library(reticulate)
library(scrubletR)
library(ggplot2)
library(patchwork)
library(dplyr)
library(data.table)
library(clustree)
library(reshape2)
library(tidyr)
library(gridExtra)
library(stringr)
library(plyr)
source("C:/Ryan/GitHub/trachtenberg-lab/transcriptomics/tools/seurat_functions.R")
source("C:/Ryan/GitHub/trachtenberg-lab/transcriptomics/tools/seurat_integration_functions.R")
source("C:/Ryan/GitHub/trachtenberg-lab/transcriptomics/xgboost/xgboost_train.R")
source("C:/Ryan/GitHub/trachtenberg-lab/transcriptomics/xgboost/plottingFxns.R")

classes <- c("Glutamatergic", "GABAergic", "Nonneuronal")
objs.mouse <- c()
objs.opossum <- c()
objs.i.mouse <- c()
objs.i.opossum <- c()
objs.m.mouse <- c()
objs.m.opossum <- c()

for (cl in classes) {

  obj.opossum <- readRDS(paste0("E:/Transcriptomics_V1/Opossum/seurat/opossum_v1_", tolower(cl), "_processed.rds"))
  obj.opossum$species <- "Opossum"
  obj.mouse.P38 <- readRDS(paste0("E:/Transcriptomics_V1/Mouse/seurat/mouse_v1_P38_", tolower(cl), "_processed.rds"))
  obj.mouse.P38$species <- "Mouse"

  objs <- list(obj.opossum, obj.mouse.P38)
  obj.integrated <- IntegrateObjects(objs[[1]], objs[[2]], resolutions = c(1, 2), nfeatures = 3000, subsample = TRUE)
  objs.i <- SplitObject(obj.integrated, split.by = "species")
  objs.m <- MapObjects(objs.i[["Opossum"]], objs.i[["Mouse"]], c("subclass", "type"), assay = "integrated")
  objs.mouse <- append(objs.mouse, objs[[2]])
  objs.opossum <- append(objs.opossum, objs[[1]])
  objs.i.mouse <- append(objs.i.mouse, objs.i[["Mouse"]])
  objs.i.opossum <- append(objs.i.opossum, objs.i[["Opossum"]])
  objs.m.mouse <- append(objs.m.mouse, objs.m[[2]])
  objs.m.opossum <- append(objs.m.opossum, objs.m[[1]])
  
}

obj.mouse <- merge(objs.mouse[[1]], y = c(objs.mouse[[2]], objs.mouse[[3]]))
obj.opossum <- merge(objs.opossum[[1]], y = c(objs.opossum[[2]], objs.opossum[[3]]))
obj.m.mouse <- merge(objs.m.mouse[[1]], y = c(objs.m.mouse[[2]], objs.m.mouse[[3]]))
obj.m.opossum <- merge(objs.m.opossum[[1]], y = c(objs.m.opossum[[2]], objs.m.opossum[[3]]))

```


```{r}

obj.mouse <- ClusterSCT(obj.mouse, c(1))
obj.m.mouse <- ClusterSCT(obj.m.mouse, c(1))
obj.opossum <- ClusterSCT(obj.opossum, c(1))
obj.m.opossum <- ClusterSCT(obj.m.opossum, c(1))

```


```{r}

obj.mouse <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_mouse.rds")
obj.opossum <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_opossum.rds")
objs.i.mouse <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_i_mouse.rds")
objs.i.opossum <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_i_opossum.rds")
objs.m.mouse <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/objs_m_mouse.rds")
objs.m.opossum <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/objs_m_opossum.rds")
obj.m.mouse <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_m_mouse.rds")
obj.m.opossum <- readRDS("E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_m_opossum.rds")

```


```{r}

colors_list <- list(
              # Glutamatergic
              IT = "#FF6C88",
              IT_A = "#FFB3B3",
              `L2/3` = "#FFB3B3",
              IT_B = "#FFA07A",
              L4 = "#FF7F50",
              IT_C = "#FF7F50",
              L5IT = "#FFA07A",
              IT_D = "#FF6347",
              L6IT = "#FF6347",
              L5NP = "#FF4500",
              L5PT = "#FF8C69",
              L6CT = "#FFA07A",
              L6b = "#FF6347",
            
              # GABAergic
              Pvalb = "#1E90FF",
              Sst = "#87CEEB",
              Vip = "#87CEFA",
              Lamp5 = "#4682B4",
              Frem1 = "#ADD8E6",
              Stac = "#5F9EA0",
            
              # Non-neuronal
              Astro = "#8C8C8C",
              Micro = "#A0A0A0",
              OD = "#B4B4B4",
              OPC = "#C8C8C8",
              Endo = "#505050",
              VLMC = "#B4B4B4"
)

```


```{r}

obj.mouse$species <- "Mouse"
obj.opossum$species <- "Opossum"
obj.combined <- merge(obj.mouse, y = obj.opossum)
obj.combined$subclass.plot <- obj.combined$subclass
obj.combined$subclass.plot[obj.combined$subclass %in% c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E", 
                                                        "L2/3", "L4", "L5IT", "L6IT")] <- "IT"

```


```{r, fig.width=10, fig.height=8}

Idents(obj.combined) <- "subclass.plot"
levels(obj.combined) <- rev(c("IT", "L5NP", "L5PT", "L6CT", "L6b",
                              "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac", 
                              "Astro", "Micro", "OD", "OPC", "Endo", "VLMC"))
DotPlot(obj.combined, assay = "RNA", features = c("Snap25", "Sv2b", "Cux2", "Rorb", "Deptor", "Etv1", "Trpc4", "Foxp2", "Lin28b", 
                                                  "Gad1", "Myo5b", "Sst", "Vip", "Lamp5", "Frem1", "Stac", 
                                                  "Aldh1l1", "Cx3cr1", "Mog", "Pdgfra", "Flt1", "Slc47a1"), cols = c("#a6a6a6", "#c692b8"), split.by = "species", scale = TRUE) + theme(axis.text.x=element_text(angle = 90, vjust = 0.4))

```


```{r}

obj.opossum$subclass.plot <- obj.opossum$subclass
obj.opossum$subclass.plot[obj.opossum$subclass %in% c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E", 
                                                        "L2/3", "L4", "L5IT", "L6IT")] <- "IT"
opossum.counts <- as.data.frame.matrix(table(obj.opossum$sample, obj.opossum$subclass.plot))
opossum.counts$Stac <- 0
opossum.counts$VLMC <- 0
opossum.counts$species <- "Opossum"
opossum.counts$Glutamatergic <- rowSums(opossum.counts[, c("IT", "L5NP", "L5PT", "L6CT", "L6b")])
opossum.counts$GABAergic <- rowSums(opossum.counts[, c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac")])
opossum.counts$Nonneuronal <- rowSums(opossum.counts[, c("Astro", "Micro", "OD", "OPC", "Endo", "VLMC")])

obj.mouse$subclass.plot <- obj.mouse$subclass
obj.mouse$subclass.plot[obj.mouse$subclass %in% c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E", 
                                                        "L2/3", "L4", "L5IT", "L6IT")] <- "IT"
mouse.counts <- as.data.frame.matrix(table(obj.mouse$sample, obj.mouse$subclass.plot))
mouse.counts$species <- "Mouse"
mouse.counts$Glutamatergic <- rowSums(mouse.counts[, c("IT", "L5NP", "L5PT", "L6CT", "L6b")])
mouse.counts$GABAergic <- rowSums(mouse.counts[, c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac")])
mouse.counts$Nonneuronal <- rowSums(mouse.counts[, c("Astro", "Micro", "OD", "OPC", "Endo", "VLMC")])

all.counts <- rbind(opossum.counts, mouse.counts)

all.props <- all.counts %>%
  mutate(IT = IT / Glutamatergic,
         L5NP = L5NP / Glutamatergic,
         L5PT = L5PT / Glutamatergic,
         L6CT = L6CT / Glutamatergic,
         L6b = L6b / Glutamatergic,
         Pvalb = Pvalb / GABAergic,
         Sst = Sst / GABAergic,
         Vip = Vip / GABAergic,
         Lamp5 = Lamp5 / GABAergic,
         Frem1 = Frem1 / GABAergic, 
         Stac = Stac / GABAergic, 
         Astro = Astro / Nonneuronal,
         Micro = Micro / Nonneuronal,
         OD = OD / Nonneuronal,
         OPC = OPC / Nonneuronal,
         Endo = Endo / Nonneuronal
         )

scatter.props <- all.props

all.props.long <- all.props %>%
                  pivot_longer(
                    cols = c("IT", "L5NP", "L5PT", "L6CT", "L6b", 
                             "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac", 
                             "Astro", "Micro", "OD", "OPC", "Endo", "VLMC", 
                             "Glutamatergic", "GABAergic", "Nonneuronal"),
                    names_to = "cell.identity", 
                    values_to = "fraction"
                               )

```


```{r, fig.height=3, fig.width=4}

all.props.long$species <- factor(all.props.long$species, levels = c("Opossum", "Mouse"))

curr.props <- all.props.long[all.props.long$cell.identity %in% c("IT", "L5NP", "L5PT", "L6CT", "L6b"), ]

ggplot(curr.props, aes(x = factor(cell.identity, level = c("IT", "L5NP", "L5PT", "L6CT", "L6b")), y = fraction, fill = species)) +
    geom_bar(stat = "summary", fun = "median", position = position_dodge(width = 0.8), width = 0.75) +
    geom_point(colour = "black", position = position_dodge(width = 0.8), size = 1) +
    scale_x_discrete(labels = c("IT", "L5NP", "L5PT", "L6CT", "L6b")) +
    scale_y_continuous(breaks = c(0, 0.2, 0.4, 0.6, 0.8), expand = c(0, 0), limits = c(0, 0.80)) +
    guides(fill = guide_legend(override.aes = list(shape = NA))) +
    theme_classic() + theme(axis.text = element_text(color = "black"), legend.text = element_text(size = 10)) + 
    labs(x = NULL, y = "Fraction of Glutamatergic Cells", fill = NULL) + 
    scale_fill_manual("legend", values = c("Opossum" = "#c692b8", "Mouse" = "#aaaaaa"))

```


```{r, fig.height=3, fig.width=4.5}

all.props.long$species <- factor(all.props.long$species, levels = c("Opossum", "Mouse"))

curr.props <- all.props.long[all.props.long$cell.identity %in% c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac"), ]

ggplot(curr.props, aes(x = factor(cell.identity, level = c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac")), y = fraction, fill = species)) +
    geom_bar(stat = "summary", fun = "median", position = position_dodge(width = 0.8), width = 0.75) +
    geom_point(colour = "black", position = position_dodge(width = 0.8), size = 1) +
    scale_x_discrete(labels = c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac")) +
    scale_y_continuous(breaks = c(0, 0.2, 0.4, 0.6), expand = c(0, 0), limits = c(0, 0.60)) +
    guides(fill = guide_legend(override.aes = list(shape = NA))) +
    theme_classic() + theme(axis.text = element_text(color = "black"), legend.text = element_text(size = 10)) + 
    labs(x = NULL, y = "Fraction of GABAergic Cells", fill = NULL) + 
    scale_fill_manual("legend", values = c("Opossum" = "#c692b8", "Mouse" = "#aaaaaa"))

```


```{r, fig.height=3, fig.width=4.5}

all.props.long$species <- factor(all.props.long$species, levels = c("Opossum", "Mouse"))

curr.props <- all.props.long[all.props.long$cell.identity %in% c("Astro", "Micro", "OD", "OPC", "Endo", "VLMC"), ]

ggplot(curr.props, aes(x = factor(cell.identity, level = c("Astro", "Micro", "OD", "OPC", "Endo", "VLMC")), y = fraction, fill = species)) +
    geom_bar(stat = "summary", fun = "median", position = position_dodge(width = 0.8), width = 0.75) +
    geom_point(colour = "black", position = position_dodge(width = 0.8), size = 1) +
    scale_x_discrete(labels = c("Astro", "Micro", "OD", "OPC", "Endo", "VLMC")) +
    scale_y_continuous(breaks = c(0, 0.2, 0.4, 0.6, 0.8), expand = c(0, 0), limits = c(0, 0.60)) +
    guides(fill = guide_legend(override.aes = list(shape = NA))) +
    theme_classic() + theme(axis.text = element_text(color = "black"), legend.text = element_text(size = 10)) + 
    labs(x = NULL, y = "Fraction of Nonneuronal Cells", fill = NULL) + 
    scale_fill_manual("legend", values = c("Opossum" = "#c692b8", "Mouse" = "#aaaaaa"))

```


```{r}

opossum.counts <- as.data.frame.matrix(table(obj.opossum.IT$sample, obj.opossum.IT$subclass))
opossum.counts$`L2/3` <- opossum.counts$IT_A
opossum.counts$L4 <- opossum.counts$IT_C
opossum.counts$L5IT <- opossum.counts$IT_B
opossum.counts$L6IT <- opossum.counts$IT_D
opossum.counts$species <- "Opossum"
opossum.counts$Glutamatergic <- rowSums(opossum.counts[, c("L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b")])

mouse.counts <- as.data.frame.matrix(table(obj.mouse.IT$sample, obj.mouse.IT$subclass))
mouse.counts$IT_A <- mouse.counts$`L2/3`
mouse.counts$IT_C <- mouse.counts$L4
mouse.counts$IT_B <- mouse.counts$L5IT
mouse.counts$IT_D <- mouse.counts$L6IT
mouse.counts$species <- "Mouse"
mouse.counts$Glutamatergic <- rowSums(mouse.counts[, c("L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b")])

all.counts <- rbind(opossum.counts, mouse.counts)

all.props <- all.counts %>%
  mutate(`L2/3` = `L2/3` / Glutamatergic,
         L4 = L4 / Glutamatergic,
         L5IT = L5IT / Glutamatergic,
         L6IT = L6IT / Glutamatergic,
         L5NP = L5NP / Glutamatergic,
         L5PT = L5PT / Glutamatergic,
         L6CT = L6CT / Glutamatergic,
         L6b = L6b / Glutamatergic
         )

scatter.props <- all.props

all.props.long <- all.props %>%
                  pivot_longer(
                    cols = c("L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b"),
                    names_to = "cell.identity", 
                    values_to = "fraction"
                               )

```


```{r, fig.height=3, fig.width=4.5}

all.props.long$species <- factor(all.props.long$species, levels = c("Opossum", "Mouse"))

curr.props <- all.props.long[all.props.long$cell.identity %in% c("L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b"), ]

ggplot(curr.props, aes(x = factor(cell.identity, level = c("L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b")), y = fraction, fill = species)) +
    geom_bar(stat = "summary", fun = "median", position = position_dodge(width = 0.8), width = 0.75) +
    geom_point(colour = "black", position = position_dodge(width = 0.8), size = 1) +
    scale_x_discrete(labels = c("L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b")) +
    scale_y_continuous(breaks = c(0, 0.2, 0.4, 0.6), expand = c(0, 0), limits = c(0, 0.60)) +
    guides(fill = guide_legend(override.aes = list(shape = NA))) +
    theme_classic() + theme(axis.text = element_text(color = "black"), legend.text = element_text(size = 10)) + 
    labs(x = NULL, y = "Fraction of Glutamatergic Cells", fill = NULL) + 
    scale_fill_manual("legend", values = c("Opossum" = "#c692b8", "Mouse" = "#aaaaaa"))

```


```{r}

DimPlot(obj.mouse, reduction = "umap", group.by = "sample", label = FALSE, raster = FALSE, shuffle = TRUE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.mouse, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.mouse, reduction = "umap", group.by = "type", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

obj.mouse$subclass.IT <- obj.mouse$subclass
obj.mouse$subclass.IT[obj.mouse$subclass %in% c("L2/3", "L4", "L5IT", "L6IT")] <-  "IT"
p <- DimPlot(obj.mouse, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_Subclass_FullSpace.png", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_Subclass_FullSpace.svg", plot = p, dpi = 300)
p <- DimPlot(obj.mouse, reduction = "umap", group.by = "subclass.IT", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_SubclassIT_FullSpace.png", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_SubclassIT_FullSpace.svg", plot = p, dpi = 300)

```


```{r}

obj.opossum$subclass.plot <- obj.opossum$subclass
obj.opossum$subclass.plot[obj.opossum$subclass %in% c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E", 
                                                      "L2/3", "L4", "L5IT", "L6IT")] <- "IT"

opossum.subclass.plot.levels <- c("IT", "L5NP", "L5PT", "L6CT", "L6b", 
                                  "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", 
                                  "Astro", "Micro", "OD", "OPC", "Endo")

opossum.subclass.levels <- c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E", "L5NP", "L5PT", "L6CT", "L6b", 
                                  "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", 
                                  "Astro", "Micro", "OD", "OPC", "Endo")

Idents(obj.opossum) <- "subclass.plot"
levels(obj.opossum) <- opossum.subclass.plot.levels
colors <- as.character(colors_list[opossum.subclass.plot.levels])
DimPlot(obj.opossum, reduction = "umap", label = TRUE, raster = FALSE, shuffle = TRUE, cols = colors) + NoLegend() + xlim(-17, 13) + ylim(-13, 17) + coord_equal()

```


```{r}

opossum.subclass.levels <- c("IT_A", "IT_B", "IT_C", "IT_D", "L5NP", "L5PT", "L6CT", "L6b", 
                             "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", 
                             "Astro", "Micro", "OD", "OPC", "Endo")

Idents(obj.m.opossum) <- "subclass"
levels(obj.m.opossum) <- opossum.subclass.levels
colors <- as.character(colors_list[opossum.subclass.levels])
DimPlot(obj.m.opossum, reduction = "umap", label = TRUE, raster = FALSE, shuffle = TRUE, cols = colors) + NoLegend() + xlim(-16, 13) + ylim(-12, 17) + coord_equal()

```


```{r}

mouse.subclass.levels <- c("L2/3", "L4", "L6IT", "L5PT", "L6CT", "L6b", 
                           "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac",
                           "Astro", "Micro", "OD", "OPC", "Endo")

Idents(obj.m.opossum) <- "predicted.subclass"
levels(obj.m.opossum) <- mouse.subclass.levels
colors <- as.character(colors_list[mouse.subclass.levels])
DimPlot(obj.m.opossum, reduction = "umap", label = TRUE, raster = FALSE, shuffle = TRUE, cols = colors) + NoLegend() + xlim(-16, 13) + ylim(-12, 17) + coord_equal()

```


```{r}

obj.mouse$subclass.plot <- obj.mouse$subclass
obj.mouse$subclass.plot[obj.mouse$subclass %in% c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E", 
                                                      "L2/3", "L4", "L5IT", "L6IT")] <- "IT"

mouse.subclass.plot.levels <- c("IT", "L5NP", "L5PT", "L6CT", "L6b", 
                                "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac",
                                "Astro", "Micro", "OD", "OPC", "Endo", "VLMC")

mouse.subclass.levels <- c("L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b", 
                           "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac",
                           "Astro", "Micro", "OD", "OPC", "Endo", "VLMC")

Idents(obj.mouse) <- "subclass.plot"
levels(obj.mouse) <- mouse.subclass.plot.levels
colors <- as.character(colors_list[mouse.subclass.plot.levels])
DimPlot(obj.mouse, reduction = "umap", label = TRUE, raster = FALSE, shuffle = TRUE, cols = colors) + NoLegend() + xlim(-16, 16) + ylim(-17, 15) + coord_equal()

```


```{r}

mouse.subclass.levels <- c("L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b", 
                           "Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac", 
                           "Astro", "Micro", "OD", "OPC", "Endo", "VLMC")

Idents(obj.m.mouse) <- "subclass"
levels(obj.m.mouse) <- mouse.subclass.levels
colors <- as.character(colors_list[mouse.subclass.levels])
DimPlot(obj.m.mouse, reduction = "umap", label = TRUE, raster = FALSE, shuffle = TRUE, cols = colors) + NoLegend() + xlim(-14, 18) + ylim(-16, 16) + coord_equal()

```


```{r}

DimPlot(obj.m.mouse, reduction = "umap", group.by = "sample", label = FALSE, raster = FALSE, shuffle = TRUE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.mouse, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.mouse, reduction = "umap", group.by = "predicted.subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.mouse, reduction = "umap", group.by = "type", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

p <- DimPlot(obj.m.mouse, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_Subclass_IntSpace.svg", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_Subclass_IntSpace.png", plot = p, dpi = 300)
p <- DimPlot(obj.m.mouse, reduction = "umap", group.by = "predicted.subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_PredictedSubclass_IntSpace.svg", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Mouse-P38_V1_All_UMAP_PredictedSubclass_IntSpace.png", plot = p, dpi = 300)

```


```{r}

DimPlot(obj.opossum, reduction = "umap", group.by = "sample", label = FALSE, raster = FALSE, shuffle = TRUE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.opossum, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.opossum, reduction = "umap", group.by = "type", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

obj.opossum$subclass.IT <- obj.opossum$subclass
obj.opossum$subclass.IT[obj.opossum$subclass %in% c("IT_A", "IT_B", "IT_C", "IT_D", "IT_E")] <-  "IT"
p <- DimPlot(obj.opossum, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_Subclass_FullSpace.png", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_Subclass_FullSpace.svg", plot = p, dpi = 300)
p <- DimPlot(obj.opossum, reduction = "umap", group.by = "subclass.IT", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_SubclassIT_FullSpace.png", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_SubclassIT_FullSpace.svg", plot = p, dpi = 300)

```


```{r}

DimPlot(obj.m.opossum, reduction = "umap", group.by = "sample", label = FALSE, raster = FALSE, shuffle = TRUE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.opossum, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.opossum, reduction = "umap", group.by = "predicted.subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
DimPlot(obj.m.opossum, reduction = "umap", group.by = "type", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()

p <- DimPlot(obj.m.opossum, reduction = "umap", group.by = "subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_Subclass_IntSpace.svg", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_Subclass_IntSpace.png", plot = p, dpi = 300)
p <- DimPlot(obj.m.opossum, reduction = "umap", group.by = "predicted.subclass", label = TRUE, raster = FALSE, shuffle = TRUE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_PredictedSubclass_IntSpace.svg", plot = p, dpi = 300)
ggsave("E:/Opossum_Paper/Figure 1/Opossum_V1_All_UMAP_PredictedSubclass_IntSpace.png", plot = p, dpi = 300)

```


```{r}

PlotMapping(list(objs.m.mouse[[1]], objs.m.opossum[[1]]), ident.order = c("IT_A", "IT_B", "IT_C", "IT_D", "L2/3", "L4", "L5IT", "L6IT", "L5NP", "L5PT", "L6CT", "L6b"))

```


```{r, fig.width=5, fig.height=5}

PlotMapping(list(objs.m.mouse[[2]], objs.m.opossum[[2]]), ident.order = c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1", "Stac"))

```


```{r, fig.width=5, fig.height=5}

PlotMapping(list(objs.m.mouse[[3]], objs.m.opossum[[3]]), ident.order = c("Astro", "Micro", "OD", "OPC", "Endo", "VLMC"))

```


```{r}

opossum.glutamatergic.levels <- c("IT_A", "IT_B", "IT_C", "IT_D", "L5NP", "L5PT", "L6CT", "L6b")
Idents(objs.m.opossum[[1]]) <- "subclass"
levels(objs.m.opossum[[1]]) <- opossum.glutamatergic.levels
VlnPlot(objs.m.opossum[[1]], "predicted.subclass.score", cols = colors_list[opossum.glutamatergic.levels]) + theme(legend.spacing.x = unit(3.0, 'cm'))

```


```{r}

opossum.gabaergic.levels <- c("Pvalb", "Sst", "Vip", "Lamp5", "Frem1")
Idents(objs.m.opossum[[2]]) <- "subclass"
levels(objs.m.opossum[[2]]) <- opossum.gabaergic.levels
VlnPlot(objs.m.opossum[[2]], "predicted.subclass.score", cols = colors_list[opossum.gabaergic.levels]) + theme(legend.spacing.x = unit(6.5, 'cm'))

```


```{r}

opossum.nonneuronal.levels <- c("Astro", "Micro", "OD", "OPC", "Endo")
Idents(objs.m.opossum[[3]]) <- "subclass"
levels(objs.m.opossum[[3]]) <- opossum.nonneuronal.levels
VlnPlot(objs.m.opossum[[3]], "predicted.subclass.score", cols = colors_list[opossum.nonneuronal.levels]) + theme(legend.spacing.x = unit(6.5, 'cm'))

```

```{r}

saveRDS(obj.mouse, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_mouse.rds")
saveRDS(obj.opossum, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_opossum.rds")
saveRDS(objs.i.mouse, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_i_mouse.rds")
saveRDS(objs.i.opossum, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_i_opossum.rds")
saveRDS(objs.m.mouse, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/objs_m_mouse.rds")
saveRDS(objs.m.opossum, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/objs_m_opossum.rds")
saveRDS(obj.m.mouse, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_m_mouse.rds")
saveRDS(obj.m.opossum, "E:/Transcriptomics_V1/Integration/Opossum_Mouse-P38/obj_m_opossum.rds")

```

